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SAEHD: removed 'dst_denoise' option. Added -t arhi option.
This commit is contained in:
iperov
parent
01f1a084b4
commit
6e094d873d
2 changed files with 136 additions and 73 deletions
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@ -7,6 +7,10 @@ class DeepFakeArchi(nn.ArchiBase):
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mod None - default
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'quick'
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opts ''
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''
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't'
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"""
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def __init__(self, resolution, use_fp16=False, mod=None, opts=None):
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super().__init__()
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@ -81,18 +85,41 @@ class DeepFakeArchi(nn.ArchiBase):
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super().__init__(**kwargs)
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def on_build(self):
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self.down1 = DownscaleBlock(self.in_ch, self.e_ch, n_downscales=4, kernel_size=5)
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if 't' in opts:
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self.down1 = Downscale(self.in_ch, self.e_ch, kernel_size=5)
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self.res1 = ResidualBlock(self.e_ch)
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self.down2 = Downscale(self.e_ch, self.e_ch*2, kernel_size=5)
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self.down3 = Downscale(self.e_ch*2, self.e_ch*4, kernel_size=5)
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self.down4 = Downscale(self.e_ch*4, self.e_ch*8, kernel_size=5)
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self.down5 = Downscale(self.e_ch*8, self.e_ch*8, kernel_size=5)
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self.res5 = ResidualBlock(self.e_ch*8)
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else:
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self.down1 = DownscaleBlock(self.in_ch, self.e_ch, n_downscales=4 if 't' not in opts else 5, kernel_size=5)
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def forward(self, x):
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if use_fp16:
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x = tf.cast(x, tf.float16)
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x = nn.flatten(self.down1(x))
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if 't' in opts:
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x = self.down1(x)
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x = self.res1(x)
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x = self.down2(x)
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x = self.down3(x)
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x = self.down4(x)
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x = self.down5(x)
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x = self.res5(x)
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else:
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x = self.down1(x)
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x = nn.flatten(x)
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if 'u' in opts:
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x = nn.pixel_norm(x, axes=-1)
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if use_fp16:
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x = tf.cast(x, tf.float32)
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return x
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def get_out_res(self, res):
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return res // (2**4)
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return res // ( (2**4) if 't' not in opts else (2**5) )
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def get_out_ch(self):
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return self.e_ch * 8
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@ -107,48 +134,46 @@ class DeepFakeArchi(nn.ArchiBase):
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def on_build(self):
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in_ch, ae_ch, ae_out_ch = self.in_ch, self.ae_ch, self.ae_out_ch
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if 'u' in opts:
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self.dense_norm = nn.DenseNorm()
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self.dense1 = nn.Dense( in_ch, ae_ch )
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self.dense2 = nn.Dense( ae_ch, lowest_dense_res * lowest_dense_res * ae_out_ch )
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if 't' not in opts:
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self.upscale1 = Upscale(ae_out_ch, ae_out_ch)
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def forward(self, inp):
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x = inp
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if 'u' in opts:
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x = self.dense_norm(x)
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x = self.dense1(x)
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x = self.dense2(x)
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x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, self.ae_out_ch)
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if use_fp16:
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x = tf.cast(x, tf.float16)
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if 't' not in opts:
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x = self.upscale1(x)
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return x
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def get_out_res(self):
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return lowest_dense_res * 2
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return lowest_dense_res * 2 if 't' not in opts else lowest_dense_res
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def get_out_ch(self):
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return self.ae_out_ch
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class Decoder(nn.ModelBase):
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def on_build(self, in_ch, d_ch, d_mask_ch):
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if 't' not in opts:
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self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale1 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale2 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res1 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res2 = ResidualBlock(d_ch*2, kernel_size=3)
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self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
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self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
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self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
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self.upscalem2 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
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self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
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self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
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if 'd' in opts:
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self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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@ -158,6 +183,32 @@ class DeepFakeArchi(nn.ArchiBase):
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self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
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else:
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self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
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else:
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self.upscale0 = Upscale(in_ch, d_ch*8, kernel_size=3)
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self.upscale1 = Upscale(d_ch*8, d_ch*8, kernel_size=3)
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self.upscale2 = Upscale(d_ch*8, d_ch*4, kernel_size=3)
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self.upscale3 = Upscale(d_ch*4, d_ch*2, kernel_size=3)
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self.res0 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res1 = ResidualBlock(d_ch*8, kernel_size=3)
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self.res2 = ResidualBlock(d_ch*4, kernel_size=3)
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self.res3 = ResidualBlock(d_ch*2, kernel_size=3)
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self.upscalem0 = Upscale(in_ch, d_mask_ch*8, kernel_size=3)
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self.upscalem1 = Upscale(d_mask_ch*8, d_mask_ch*8, kernel_size=3)
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self.upscalem2 = Upscale(d_mask_ch*8, d_mask_ch*4, kernel_size=3)
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self.upscalem3 = Upscale(d_mask_ch*4, d_mask_ch*2, kernel_size=3)
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self.out_conv = nn.Conv2D( d_ch*2, 3, kernel_size=1, padding='SAME', dtype=conv_dtype)
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if 'd' in opts:
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self.out_conv1 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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self.out_conv2 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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self.out_conv3 = nn.Conv2D( d_ch*2, 3, kernel_size=3, padding='SAME', dtype=conv_dtype)
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self.upscalem4 = Upscale(d_mask_ch*2, d_mask_ch*1, kernel_size=3)
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self.out_convm = nn.Conv2D( d_mask_ch*1, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
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else:
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self.out_convm = nn.Conv2D( d_mask_ch*2, 1, kernel_size=1, padding='SAME', dtype=conv_dtype)
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def forward(self, z):
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x = self.upscale0(z)
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@ -167,6 +218,10 @@ class DeepFakeArchi(nn.ArchiBase):
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x = self.upscale2(x)
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x = self.res2(x)
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if 't' in opts:
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x = self.upscale3(x)
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x = self.res3(x)
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if 'd' in opts:
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x = tf.nn.sigmoid( nn.depth_to_space(tf.concat( (self.out_conv(x),
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self.out_conv1(x),
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@ -179,8 +234,15 @@ class DeepFakeArchi(nn.ArchiBase):
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m = self.upscalem0(z)
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m = self.upscalem1(m)
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m = self.upscalem2(m)
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if 't' in opts:
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m = self.upscalem3(m)
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if 'd' in opts:
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m = self.upscalem4(m)
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else:
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if 'd' in opts:
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m = self.upscalem3(m)
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m = tf.nn.sigmoid(self.out_convm(m))
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if use_fp16:
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@ -35,9 +35,7 @@ class SAEHDModel(ModelBase):
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
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default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
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archi = self.load_or_def_option('archi', 'liae-ud')
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archi = {'dfuhd':'df-u','liaeuhd':'liae-u'}.get(archi, archi) #backward comp
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default_archi = self.options['archi'] = archi
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default_archi = self.options['archi'] = self.load_or_def_option('archi', 'liae-ud')
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default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
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default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
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@ -47,7 +45,6 @@ class SAEHDModel(ModelBase):
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default_eyes_mouth_prio = self.options['eyes_mouth_prio'] = self.load_or_def_option('eyes_mouth_prio', False)
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default_uniform_yaw = self.options['uniform_yaw'] = self.load_or_def_option('uniform_yaw', False)
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default_blur_out_mask = self.options['blur_out_mask'] = self.load_or_def_option('blur_out_mask', False)
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default_dst_denoise = self.options['dst_denoise'] = self.load_or_def_option('dst_denoise', False)
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default_adabelief = self.options['adabelief'] = self.load_or_def_option('adabelief', True)
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@ -107,7 +104,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if archi_opts is not None:
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if len(archi_opts) == 0:
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continue
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if len([ 1 for opt in archi_opts if opt not in ['u','d'] ]) != 0:
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if len([ 1 for opt in archi_opts if opt not in ['u','d','t'] ]) != 0:
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continue
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if 'd' in archi_opts:
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@ -141,7 +138,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['eyes_mouth_prio'] = io.input_bool ("Eyes and mouth priority", default_eyes_mouth_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction. Also makes the detail of the teeth higher.')
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.')
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self.options['dst_denoise'] = io.input_bool ("Denoise DST faceset.", default_dst_denoise, help_message='Used in RTM(ReadyToMerge) training with RTM DST faceset. Removes high frequency noise keeping edges. Result is better face syncronization with any face. Can be enabled at any time.')
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
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@ -233,7 +229,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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random_src_flip = self.random_src_flip if not self.pretrain else True
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random_dst_flip = self.random_dst_flip if not self.pretrain else True
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blur_out_mask = self.options['blur_out_mask']
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dst_denoise = self.options['dst_denoise']
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learn_dst_bg = False#True
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if self.pretrain:
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self.options_show_override['gan_power'] = 0.0
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@ -327,8 +323,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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elif 'liae' in archi_type:
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
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self.src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
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self.src_dst_opt.initialize_variables (self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
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self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
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@ -413,6 +407,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
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gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
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gpu_pred_dst_dst_no_code_grad, _ = self.decoder(tf.stop_gradient(gpu_dst_code))
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
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gpu_pred_src_src_list.append(gpu_pred_src_src)
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@ -425,25 +420,30 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
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gpu_target_srcm_blur = tf.clip_by_value(gpu_target_srcm_blur, 0, 0.5) * 2
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gpu_target_srcm_anti_blur = 1.0-gpu_target_srcm_blur
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gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
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gpu_target_dstm_style_blur = gpu_target_dstm_blur #default style mask is 0.5 on boundary
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gpu_target_dstm_style_anti_blur = 1.0 - gpu_target_dstm_style_blur
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gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
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gpu_target_dstm_anti_blur = 1.0-gpu_target_dstm_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_style_masked = gpu_target_dst*gpu_target_dstm_style_blur
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gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
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gpu_target_dst_style_anti_masked = gpu_target_dst*gpu_target_dstm_style_anti_blur
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gpu_target_src_anti_masked = gpu_target_src*gpu_target_srcm_anti_blur
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gpu_target_dst_anti_masked = gpu_target_dst*gpu_target_dstm_anti_blur
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gpu_pred_src_src_anti_masked = gpu_pred_src_src*gpu_target_srcm_anti_blur
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gpu_pred_dst_dst_anti_masked = gpu_pred_dst_dst*gpu_target_dstm_anti_blur
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gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_blur)
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gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
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gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
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gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
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gpu_pred_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
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gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
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gpu_psd_target_dst_style_masked = gpu_pred_src_dst*gpu_target_dstm_style_blur
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gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur)
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gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*gpu_target_dstm_style_anti_blur
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if resolution < 256:
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gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
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@ -483,6 +483,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss = gpu_src_loss + gpu_dst_loss
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if learn_dst_bg and masked_training and 'liae' in archi_type:
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gpu_G_loss += tf.reduce_mean( tf.square(gpu_pred_dst_dst_no_code_grad*gpu_target_dstm_anti_blur-gpu_target_dst_anti_masked),axis=[1,2,3] )
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def DLoss(labels,logits):
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return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits), axis=[1,2,3])
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@ -526,8 +529,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
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DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
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if masked_training:
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# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
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gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
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@ -536,6 +537,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights )]
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# Average losses and gradients, and create optimizer update ops
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with tf.device(f'/CPU:0'):
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pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
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@ -560,7 +563,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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# Initializing training and view functions
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def src_dst_train(warped_src, target_src, target_srcm, target_srcm_em, \
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warped_dst, target_dst, target_dstm, target_dstm_em, ):
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s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
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s, d = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
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feed_dict={self.warped_src :warped_src,
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self.target_src :target_src,
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self.target_srcm:target_srcm,
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@ -569,7 +572,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.target_dst :target_dst,
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self.target_dstm:target_dstm,
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self.target_dstm_em:target_dstm_em,
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})
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})[:2]
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return s, d
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self.src_dst_train = src_dst_train
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@ -674,7 +677,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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sample_process_options=SampleProcessor.Options(random_flip=random_dst_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':random_warp, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'denoise_filter' : dst_denoise, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.EYES_MOUTH, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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@ -762,13 +764,13 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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bs = self.get_batch_size()
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( (warped_src, target_src, target_srcm, target_srcm_em), \
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(warped_dst, target_dst, target_dst_train, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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(warped_dst, target_dst, target_dstm, target_dstm_em) ) = self.generate_next_samples()
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst_train, target_dstm, target_dstm_em)
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src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
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for i in range(bs):
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self.last_src_samples_loss.append ( (src_loss[i], target_src[i], target_srcm[i], target_srcm_em[i],) )
|
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self.last_dst_samples_loss.append ( (dst_loss[i], target_dst[i], target_dst_train[i], target_dstm[i], target_dstm_em[i],) )
|
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self.last_dst_samples_loss.append ( (dst_loss[i], target_dst[i], target_dstm[i], target_dstm_em[i],) )
|
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if len(self.last_src_samples_loss) >= bs*16:
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src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(0), reverse=True)
|
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|
|
@ -779,11 +781,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
target_srcm_em = np.stack( [ x[3] for x in src_samples_loss[:bs] ] )
|
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|
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target_dst = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
|
||||
target_dst_train = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm_em = np.stack( [ x[4] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
|
||||
target_dstm_em = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
|
||||
|
||||
src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst_train, target_dstm, target_dstm_em)
|
||||
src_loss, dst_loss = self.src_dst_train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst, target_dstm, target_dstm_em)
|
||||
self.last_src_samples_loss = []
|
||||
self.last_dst_samples_loss = []
|
||||
|
||||
|
|
@ -791,14 +792,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
self.D_train (warped_src, warped_dst)
|
||||
|
||||
if self.gan_power != 0:
|
||||
self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst_train, target_dstm, target_dstm_em)
|
||||
self.D_src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
|
||||
|
||||
return ( ('src_loss', np.mean(src_loss) ), ('dst_loss', np.mean(dst_loss) ), )
|
||||
|
||||
#override
|
||||
def onGetPreview(self, samples, for_history=False):
|
||||
( (warped_src, target_src, target_srcm, target_srcm_em),
|
||||
(warped_dst, target_dst, target_dst_train, target_dstm, target_dstm_em) ) = samples
|
||||
(warped_dst, target_dst, target_dstm, target_dstm_em) ) = samples
|
||||
|
||||
S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
|
||||
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue